|
import os.path |
|
import pickle |
|
from pathlib import Path |
|
from typing import Any, Callable, Optional, Tuple, Union |
|
|
|
import numpy as np |
|
from PIL import Image |
|
|
|
from torchvision.datasets.utils import check_integrity, download_and_extract_archive |
|
from torchvision.datasets.vision import VisionDataset |
|
|
|
|
|
class CINIC10(VisionDataset): |
|
"""`CINIC10 <https://github.com/BayesWatch/cinic-10>`_ Dataset. |
|
|
|
Args: |
|
root (str or ``pathlib.Path``): Root directory of dataset where directory |
|
``cinic-10-batches-py`` exists or will be saved to if download is set to True. |
|
train (bool, optional): If True, creates dataset from training set, otherwise |
|
creates from test set. |
|
transform (callable, optional): A function/transform that takes in a PIL image |
|
and returns a transformed version. E.g, ``transforms.RandomCrop`` |
|
target_transform (callable, optional): A function/transform that takes in the |
|
target and transforms it. |
|
download (bool, optional): If true, downloads the dataset from the internet and |
|
puts it in root directory. If dataset is already downloaded, it is not |
|
downloaded again. |
|
|
|
""" |
|
|
|
base_folder = "cinic-10-batches-py" |
|
url = "https://huggingface.co./datasets/alexey-zhavoronkin/CINIC10/resolve/main/cinic-10-python.tar.gz?download=true" |
|
filename = "cinic-10-python.tar.gz" |
|
tgz_md5 = None |
|
train_list = [ |
|
["data_batch_1", None], |
|
["data_batch_2", None], |
|
["data_batch_3", None], |
|
["data_batch_4", None], |
|
["data_batch_5", None], |
|
["data_batch_6", None], |
|
["data_batch_7", None], |
|
["data_batch_8", None], |
|
["data_batch_9", None], |
|
["data_batch_10", None], |
|
["data_batch_11", None], |
|
["data_batch_12", None], |
|
["data_batch_13", None], |
|
["data_batch_14", None], |
|
|
|
|
|
] |
|
|
|
test_list = [ |
|
["test_batch_1", None], |
|
["test_batch_2", None], |
|
["test_batch_3", None], |
|
["test_batch_4", None], |
|
["test_batch_5", None], |
|
["test_batch_6", None], |
|
["test_batch_7", None], |
|
|
|
|
|
] |
|
meta = { |
|
"filename": "batches.meta", |
|
"key": "label_names", |
|
"md5": None, |
|
} |
|
|
|
def __init__( |
|
self, |
|
root: Union[str, Path], |
|
train: bool = True, |
|
transform: Optional[Callable] = None, |
|
target_transform: Optional[Callable] = None, |
|
download: bool = False, |
|
) -> None: |
|
|
|
super().__init__(root, transform=transform, target_transform=target_transform) |
|
|
|
self.train = train |
|
|
|
if download: |
|
self.download() |
|
|
|
if not self._check_integrity(): |
|
raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") |
|
|
|
if self.train: |
|
downloaded_list = self.train_list |
|
else: |
|
downloaded_list = self.test_list |
|
|
|
self.data: Any = [] |
|
self.targets = [] |
|
|
|
|
|
for file_name, checksum in downloaded_list: |
|
file_path = os.path.join(self.root, self.base_folder, file_name) |
|
with open(file_path, "rb") as f: |
|
entry = pickle.load(f, encoding="latin1") |
|
self.data.append(entry["data"]) |
|
if "labels" in entry: |
|
self.targets.extend(entry["labels"]) |
|
else: |
|
self.targets.extend(entry["fine_labels"]) |
|
|
|
self.data = np.vstack(self.data).reshape(-1, 3, 32, 32) |
|
self.data = self.data.transpose((0, 2, 3, 1)) |
|
|
|
self._load_meta() |
|
|
|
def _load_meta(self) -> None: |
|
path = os.path.join(self.root, self.base_folder, self.meta["filename"]) |
|
if not check_integrity(path, self.meta["md5"]): |
|
raise RuntimeError("Dataset metadata file not found or corrupted. You can use download=True to download it") |
|
with open(path, "rb") as infile: |
|
data = pickle.load(infile, encoding="latin1") |
|
self.classes = data[self.meta["key"]] |
|
self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)} |
|
|
|
def __getitem__(self, index: int) -> Tuple[Any, Any]: |
|
""" |
|
Args: |
|
index (int): Index |
|
|
|
Returns: |
|
tuple: (image, target) where target is index of the target class. |
|
""" |
|
img, target = self.data[index], self.targets[index] |
|
|
|
|
|
|
|
img = Image.fromarray(img) |
|
|
|
if self.transform is not None: |
|
img = self.transform(img) |
|
|
|
if self.target_transform is not None: |
|
target = self.target_transform(target) |
|
|
|
return img, target |
|
|
|
def __len__(self) -> int: |
|
return len(self.data) |
|
|
|
def _check_integrity(self) -> bool: |
|
for filename, md5 in self.train_list + self.test_list: |
|
fpath = os.path.join(self.root, self.base_folder, filename) |
|
if not check_integrity(fpath, md5): |
|
return False |
|
return True |
|
|
|
def download(self) -> None: |
|
if self._check_integrity(): |
|
print("Files already downloaded and verified") |
|
return |
|
download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5) |
|
|
|
def extra_repr(self) -> str: |
|
split = "Train" if self.train is True else "Test" |
|
return f"Split: {split}" |
